Overview

Dataset statistics

Number of variables12
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.4 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with invoice_quantity and 3 other fieldsHigh correlation
recency_days is highly overall correlated with invoice_quantityHigh correlation
invoice_quantity is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
items_quantity is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
products_quantity is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_unique_basket_size is highly overall correlated with products_quantity and 1 other fieldsHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.1569664)Skewed
returns is highly skewed (γ1 = 21.9754032)Skewed
frequency is highly skewed (γ1 = 24.87687084)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.1%) zerosZeros
returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-01-10 23:55:30.920784
Analysis finished2023-01-10 23:55:52.118457
Duration21.2 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.377
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:52.222827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.1445
Coefficient of variation (CV)0.11258036
Kurtosis-1.2061782
Mean15270.377
Median Absolute Deviation (MAD)1489
Skewness0.032193711
Sum45322479
Variance2955457.9
MonotonicityNot monotonic
2023-01-10T20:55:52.371009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
12670 1
 
< 0.1%
17734 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
Other values (2958) 2958
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.4851
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:52.529745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.465
Coefficient of variation (CV)3.7629558
Kurtosis397.30132
Mean2693.4851
Median Absolute Deviation (MAD)670.84
Skewness17.635372
Sum7994263.7
Variance1.0272766 × 108
MonotonicityNot monotonic
2023-01-10T20:55:52.670730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.96 2
 
0.1%
2053.02 2
 
0.1%
331 2
 
0.1%
1353.74 2
 
0.1%
889.93 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
734.94 2
 
0.1%
Other values (2943) 2948
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.309299
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:52.828701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.760922
Coefficient of variation (CV)1.2091707
Kurtosis2.7765172
Mean64.309299
Median Absolute Deviation (MAD)26
Skewness1.7980529
Sum190870
Variance6046.7611
MonotonicityNot monotonic
2023-01-10T20:55:52.981702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2218
74.7%
ValueCountFrequency (%)
0 33
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

invoice_quantity
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7243935
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:53.152231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8577599
Coefficient of variation (CV)1.5473709
Kurtosis190.78624
Mean5.7243935
Median Absolute Deviation (MAD)2
Skewness10.765555
Sum16990
Variance78.45991
MonotonicityNot monotonic
2023-01-10T20:55:53.301948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 784
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 784
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

items_quantity
Real number (ℝ)

Distinct1670
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.1044
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:53.467384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.35
Q1296
median640
Q31399.5
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1103.5

Descriptive statistics

Standard deviation5705.2914
Coefficient of variation (CV)3.6061408
Kurtosis516.7418
Mean1582.1044
Median Absolute Deviation (MAD)421
Skewness18.737654
Sum4695686
Variance32550350
MonotonicityNot monotonic
2023-01-10T20:55:53.634452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 9
 
0.3%
88 9
 
0.3%
246 8
 
0.3%
272 8
 
0.3%
84 8
 
0.3%
260 8
 
0.3%
288 8
 
0.3%
1200 7
 
0.2%
516 7
 
0.2%
Other values (1660) 2885
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%
50255 1
< 0.1%

products_quantity
Real number (ℝ)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.76449
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:53.811347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.93294
Coefficient of variation (CV)2.1987868
Kurtosis354.77884
Mean122.76449
Median Absolute Deviation (MAD)44
Skewness15.706135
Sum364365
Variance72863.79
MonotonicityNot monotonic
2023-01-10T20:55:53.961163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
35 35
 
1.2%
29 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2628
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 15
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.994257
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:54.129074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.915888
Q113.118111
median17.953447
Q324.981794
95-th percentile90.052125
Maximum4453.43
Range4451.2794
Interquartile range (IQR)11.863683

Descriptive statistics

Standard deviation119.53207
Coefficient of variation (CV)3.6228143
Kurtosis812.96474
Mean32.994257
Median Absolute Deviation (MAD)5.9790186
Skewness25.156966
Sum97926.954
Variance14287.915
MonotonicityNot monotonic
2023-01-10T20:55:54.282909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2955) 2955
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%

returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.888477
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:54.434429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.86478
Coefficient of variation (CV)8.107685
Kurtosis596.20199
Mean34.888477
Median Absolute Deviation (MAD)1
Skewness21.975403
Sum103549
Variance80012.486
MonotonicityNot monotonic
2023-01-10T20:55:54.580424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
7 43
 
1.4%
8 43
 
1.4%
Other values (203) 705
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.302133
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:54.726501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.917308
median48.267857
Q385.333333
95-th percentile200.65
Maximum366
Range365
Interquartile range (IQR)59.416026

Descriptive statistics

Standard deviation63.505358
Coefficient of variation (CV)0.94358612
Kurtosis4.9080488
Mean67.302133
Median Absolute Deviation (MAD)26.267857
Skewness2.066084
Sum199752.73
Variance4032.9306
MonotonicityNot monotonic
2023-01-10T20:55:54.879468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
11 17
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
28 16
 
0.5%
Other values (1248) 2776
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11383237
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:55.044692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088935048
Q10.016339869
median0.025898352
Q30.049478583
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033138713

Descriptive statistics

Standard deviation0.40822056
Coefficient of variation (CV)3.5861552
Kurtosis989.06632
Mean0.11383237
Median Absolute Deviation (MAD)0.012196886
Skewness24.876871
Sum337.85449
Variance0.16664402
MonotonicityNot monotonic
2023-01-10T20:55:55.198062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2635
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct1005
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.161667
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:55.361963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4021531
Q110
median17.2
Q327.75
95-th percentile56.9475
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.511925
Coefficient of variation (CV)0.88043576
Kurtosis27.710745
Mean22.161667
Median Absolute Deviation (MAD)8.2
Skewness3.5001795
Sum65775.829
Variance380.7152
MonotonicityNot monotonic
2023-01-10T20:55:55.512502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 39
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 32
 
1.1%
17 31
 
1.0%
18 30
 
1.0%
10 30
 
1.0%
5 29
 
1.0%
Other values (995) 2620
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 7
 
0.2%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

avg_basket_size
Real number (ℝ)

Distinct1978
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.25289
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-10T20:55:55.680378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172.29167
Q3281.54808
95-th percentile599.58
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)178.31058

Descriptive statistics

Standard deviation283.8932
Coefficient of variation (CV)1.2016496
Kurtosis102.78169
Mean236.25289
Median Absolute Deviation (MAD)83.041667
Skewness7.7018777
Sum701198.57
Variance80595.347
MonotonicityNot monotonic
2023-01-10T20:55:56.195199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
73 9
 
0.3%
136 8
 
0.3%
75 8
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
130 7
 
0.2%
Other values (1968) 2881
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%

Interactions

2023-01-10T20:55:50.016770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:31.250827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:32.873343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:34.555609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:36.230350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:38.094036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:39.735487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:41.400875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:42.940847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:44.544434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:46.613194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:48.396650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:50.158940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:31.378492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:33.023441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:34.695267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:36.351984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:38.233557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:39.868016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:41.529659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:43.080715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:44.685791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:46.773352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:48.525762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:50.289785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:31.510593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:33.153254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:34.832090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:36.471816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:38.362098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:40.004195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:41.661900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:43.218350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:44.830213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:46.937234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:48.668540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:50.425099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:31.646380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:33.299703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:34.977186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:36.606301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:38.502545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:40.144845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:41.794458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:43.349280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:44.985348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:47.085531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:48.810201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:50.556982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:31.765386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:33.453937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:35.104123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:36.729707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:38.624670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:40.278399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:41.907407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:43.483874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:45.120051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:47.224114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:48.935454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:50.722294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:31.908388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:33.616311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:35.262995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:36.876519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:38.773008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:40.423456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:42.048521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:43.628625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:45.273816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:47.382058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:49.087223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:50.865447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:32.050471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:33.768029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:35.422918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:37.319067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:38.917075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:40.570922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:42.185138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:43.775353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:45.425846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:47.538331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:49.228407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:50.984332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:32.172264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:33.891693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:35.557576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:37.442323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:39.038170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:40.699978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:42.305649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:43.896013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:45.555377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:47.667884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:49.351804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:51.109625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:32.298694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:34.026134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:35.682589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:37.572435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:39.173906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:40.829504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:42.424046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:44.018063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:45.678179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:47.803061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:49.480384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:51.244455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:32.446880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:34.165226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:35.821584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:37.706965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:39.315489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:40.969337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:42.554727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:44.151038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:45.819784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:47.959755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:49.612048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:51.389936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:32.587613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:34.296113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:35.959847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:37.846381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:39.463480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:41.115789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:42.685076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:44.282773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:45.970604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:48.103835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:49.749180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:51.517150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:32.727957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:34.421762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:36.095835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:37.971140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:39.596910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:41.254129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:42.811680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:44.414430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:46.476754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:48.252564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-10T20:55:49.876543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-10T20:55:56.335236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysinvoice_quantityitems_quantityproducts_quantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
customer_id1.000-0.0770.0010.026-0.0710.013-0.131-0.0640.019-0.002-0.007-0.123
gross_revenue-0.0771.000-0.4140.7720.9250.7460.2450.371-0.2490.0910.2920.574
recency_days0.001-0.4141.000-0.503-0.407-0.4360.049-0.1190.1090.017-0.108-0.097
invoice_quantity0.0260.772-0.5031.0000.7180.6900.0600.295-0.2580.0780.0250.101
items_quantity-0.0710.925-0.4070.7181.0000.7320.1660.343-0.2280.0810.3220.729
products_quantity0.0130.746-0.4360.6900.7321.000-0.3770.244-0.1650.0350.6990.384
avg_ticket-0.1310.2450.0490.0600.166-0.3771.0000.189-0.1230.091-0.6100.187
returns-0.0640.371-0.1190.2950.3430.2440.1891.000-0.3980.2350.0200.209
avg_recency_days0.019-0.2490.109-0.258-0.228-0.165-0.123-0.3981.000-0.8810.049-0.078
frequency-0.0020.0910.0170.0780.0810.0350.0910.235-0.8811.000-0.0730.028
avg_unique_basket_size-0.0070.292-0.1080.0250.3220.699-0.6100.0200.049-0.0731.0000.449
avg_basket_size-0.1230.574-0.0970.1010.7290.3840.1870.209-0.0780.0280.4491.000

Missing values

2023-01-10T20:55:51.755658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-10T20:55:52.007525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysinvoice_quantityitems_quantityproducts_quantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
0178505391.21372.034.01733.0297.018.15222240.035.50000017.0000008.73529450.970588
1130473232.5956.09.01390.0171.018.90403535.027.2500000.02830219.000000154.444444
2125836705.382.015.05028.0232.028.90250050.023.1875000.04032315.466667335.200000
313748948.2595.05.0439.028.033.8660710.092.6666670.0179215.60000087.800000
415100876.00333.03.080.03.0292.00000022.08.6000000.0731711.00000026.666667
5152914623.3025.014.02102.0102.045.32647129.023.2000000.0401157.285714150.142857
6146885630.877.021.03621.0327.017.219786399.018.3000000.05722115.571429172.428571
7178095411.9116.012.02057.061.088.71983641.035.7000000.0335205.083333171.416667
81531160767.900.091.038194.02379.025.543464474.04.1444440.24331626.142857419.714286
9160982005.6387.07.0613.067.029.9347760.047.6666670.0243909.57142987.571429
customer_idgross_revenuerecency_daysinvoice_quantityitems_quantityproducts_quantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
5626177271060.2515.01.0645.066.016.0643946.06.01.00000066.0645.000000
563617232421.522.02.0203.036.011.7088890.012.00.15384618.0101.500000
563717468137.0010.02.0116.05.027.4000000.04.00.4000002.558.000000
564813596697.045.02.0406.0166.04.1990360.07.00.25000083.0203.000000
5654148931237.859.02.0799.073.016.9568490.02.00.66666736.5399.500000
565812479473.2011.01.0382.030.015.77333334.04.01.00000030.0382.000000
567914126706.137.03.0508.015.047.07533350.03.00.7500005.0169.333333
5685135211092.391.03.0733.0435.02.5112410.04.50.300000145.0244.333333
569515060301.848.04.0262.0120.02.5153330.01.02.00000030.065.500000
571412558269.967.01.0196.011.024.541818196.06.01.00000011.0196.000000